Future Trends and Emerging Threats in Drone Cybersecurity

Sindiramutty, Siva Raja; Jhanjhi, Noor Zaman; Tan, Chong Eng; Khan, Navid Ali; Shah, Bhavin; Yun, Khor Jia; Ray, Sayan Kumar; Jazri, Husin; Hussain, Manzoor

doi:10.4018/979-8-3693-0774-8.ch007

Advances in Information Security, Privacy, and Ethics

2024

DOI: 10.4018/979-8-3693-0774-8.ch007

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Future Trends and Emerging Threats in Drone Cybersecurity

Siva Raja Sindiramutty,

Noor Zaman Jhanjhi,

Chong Eng Tan

et al.

Abstract: The rapid proliferation of drone technology has ushered in a new era of innovation and convenience across various industries. However, this technological advancement has also given rise to a host of cybersecurity challenges that demand proactive attention. This chapter delves into the dynamic landscape of drone security, exploring the evolving trends and emerging threats that shape this field. Beginning with an overview of drone technology advancements, including the integration of artificial intelligence and … Show more

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Cited by 6 publications

References 153 publications

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Generative AI in Network Security and Intrusion Detection

Sindiramutty,

Prabagaran,

Jhanjhi

et al. 2024

Advances in Information Security, Privacy, and Ethics

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Protecting virtual assets from cyber threats is essential as we live in a digitally advanced world. Providing a responsible emphasis on proper network security and intrusion detection is imperative. On the other hand, traditional strategies need a supportive tool to adapt to the transforming threat space. New generative AI techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) are the mainstream technologies required to meet the gap. This chapter deals with how these models can enhance network security by inspecting the network traffic for anomalies and malicious behaviors detected through unsupervised learning, which considers strange or emerging phenomena. This survey features innovations in fault detection, behavior control, deep packet inspection, traffic classification, and examples of real-world intrusions detected by GAN-based systems. Furthermore, the chapter focuses on the challenges of adversarial attacks on models that require the development of solid defense mechanisms, such as generative adversarial networks. Ethics becomes the following matter on our list of discussions, given that privacy transparency and accountability are to be observed when working with generative AI technologies in network security. Finally, the authors examine trends that determine how cyber-attacks are dealt with comprehensively.

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Generative AI in Network Security and Intrusion Detection

Sindiramutty,

Prabagaran,

Jhanjhi

et al. 2024

Advances in Information Security, Privacy, and Ethics

View full text Add to dashboard Cite

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Security Considerations in Generative AI for Web Applications

Sindiramutty,

Prabagaran,

Jhanjhi

et al. 2024

Advances in Information Security, Privacy, and Ethics

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Protecting AI in web applications is necessary. This domain is a composite of technology and huge scope with good prospects and immense difficulties. This chapter covers the landscape of security issues with advancing generative AI techniques for integration into web development frameworks. The initial section is on security in web development—a conversation on the subtleties of generative AI-based methods. In a literal stance, the chapter offers 13 ways to approach it. Among the threats are those that introduce security issues related to generative AI deployments, which illustrate why it is vital for defenders and infrastructure owners to implement mitigation measures proactively. This chapter pertains to the security and privacy of data and lessons for securing and preventing vulnerability. The chapter explores attacks, model poisoning, bias issues, defence mechanisms, and long-term mitigation strategies. Additionally, Service A promotes transparency, explainability, and compliance with applicable laws while structuring a development methodology and deployment methods/operation. The text outlines how to respond and recover from incidents as it provides response frameworks for everyone involved in managing security breaches. Finally, it addresses trends, possible threats, and lessons learned from real-world case studies. In order to contribute to addressing these research needs, this chapter sheds light on the security considerations associated with AI for web development and suggests recommendations that can help researchers, practitioners, and policymakers enhance the security posture of popular generative AI advancements used in generating web applications.

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Ethics and Transparency in Secure Web Model Generation

Sindiramutty,

Prabagaran,

Jhanjhi

et al. 2024

Advances in Information Security, Privacy, and Ethics

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The chapter discusses how ethics and transparency relate to creating secure web models for AI. AI plays a role in secure web development, and the authors consider ethics and transparency as two critical aspects of this subject, which influences users, stakeholders, or society. The examination begins with AI principles, which include fairness, accountability, and privacy requirements. They then get into the problems with secure web models. In this chapter, they break down bias and fairness concerns at the source and find ways to resolve them in web models. This relates to trust and accountability, where transparency and explainability are highlighted. They also provide case studies showing the effectiveness of transparent and explainable AI in increasing user engagement. They also delve into decision-making frameworks to help navigate the ethical dilemmas in AI web development. It then represents the conversation on the atmospherics of empowerment tools, such as monitoring and evaluation guidelines for mobilisation implementation practice and governance. To sum up, the authors underline the ethical and transparent views for us to do AI-driven web development. Therefore, they urge all stakeholders to make ethics and transparency the cornerstones of responsible webs.

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Future Trends and Emerging Threats in Drone Cybersecurity

Cited by 6 publications

References 153 publications

Generative AI in Network Security and Intrusion Detection

Generative AI in Network Security and Intrusion Detection

Security Considerations in Generative AI for Web Applications

Ethics and Transparency in Secure Web Model Generation

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